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Bayesian Networks: A Practical Guide to Applications

Publisher: 
John Wiley
Number of Pages: 
428
Price: 
110.00
ISBN: 
9780470060308
Date Received: 
Monday, May 19, 2008
Reviewable: 
No
Include In BLL Rating: 
No
Reviewer Email Address: 
Olivier Pourret, Patrick Naïm, and Bruce Marcot, editors
Series: 
Statistics in Practice
Publication Date: 
2008
Format: 
Hardcover

Foreword

Preface

1 Introduction to Bayesian networks

1.1 Models

1.2 Probabilistic vs. deterministic models

1.3 Unconditional and conditional independence

1.4 Bayesian networks

2 Medical diagnosis

2.1 Bayesian networks in medicine

2.2 Context and history

2.3 Model construction

2.4 Inference

2.5 Model validation

2.6 Model use

2.7 Comparison to other approaches

2.8 Conclusions and perspectives

3 Clinical decision support

3.1 Introduction

3.2 Models and methodology

3.3 The Busselton network

3.4 The PROCAMnetwork

3.5 The PROCAMBusselton network

3.6 Evaluation

3.7 The clinical support tool: TakeHeartII

3.8 Conclusion

4 Complex genetic models

4.1 Introduction

4.2 Historical perspectives

4.3 Complex traits

4.4 Bayesian networks to dissect complex traits

4.5 Applications

4.6 Future challenges

5 Crime risk factors analysis

5.1 Introduction

5.2 Analysis of the factors affecting crime risk

5.3 Expert probabilities elicitation

5.4 Data preprocessing

5.5 A Bayesian network model

5.6 Results

5.7 Accuracy assessment

5.8 Conclusions

6 Spatial dynamics in the coastal region

6.1 Introduction

6.2 An indicator-based analysis

6.3 The Bayesian network model

6.4 Conclusions

7 Inference problems in forensic science

7.1 Introduction

7.2 Building Bayesian networks for inference

7.3 Applications of Bayesian networks in forensic science

7.4 Conclusions

8 Conservation of marbled murrelets in British Columbia

8.1 Context/history

8.2 Model construction

8.3 Model calibration, validation and use

8.4 Conclusions/perspectives

9 Classifiers for modeling of mineral potential

9.1 Mineral potential mapping

9.2 Classifiers for mineral potential mapping

9.3 Bayesian network mapping of base metal deposit

9.4 Discussion

9.5 Conclusions

10 Student modeling

10.1 Introduction

10.2 Probabilistic relational models

10.3 Probabilistic relational student model

10.4 Case study

10.5 Experimental evaluation

10.6 Conclusions and future directions

11 Sensor validation

11.1 Introduction

11.2 The problem of sensor validation

11.3 Sensor validation algorithm

11.4 Gas turbines

11.5 Models learned and experimentation

11.6 Discussion and conclusion

12 An information retrieval system

12.1 Introduction

12.2 Overview

12.3 Bayesian networks and information retrieval

12.4 Theoretical foundations

12.5 Building the information retrieval system

12.6 Conclusion

13 Reliability analysis of systems

13.1 Introduction

13.2 Dynamic fault trees

13.3 Dynamic Bayesian networks

13.4 A case study: The Hypothetical Sprinkler System

13.5 Conclusions

14 Terrorism risk management

14.1 Introduction

14.2 The Risk Influence Network

14.3 Software implementation

14.4 Site Profiler deployment

14.5 Conclusion

15 Credit-rating of companies

15.1 Introduction

15.2 Naive Bayesian classifiers

15.3 Example of actual credit-ratings systems

15.4 Credit-rating data of Japanese companies

15.5 Numerical experiments

15.6 Performance comparison of classifiers

15.7 Conclusion

16 Classification of Chilean wines

16.1 Introduction

16.2 Experimental setup

16.3 Feature extraction methods

16.4 Classification results

16.5 Conclusions

17 Pavement and bridge management

17.1 Introduction

17.2 Pavement management decisions

17.3 Bridge management

17.4 Bridge approach embankment - case study

17.5 Conclusion

18 Complex industrial process operation

18.1 Introduction

18.2 A methodology for Root Cause Analysis

18.3 Pulp and paper application

18.4 The ABB Industrial IT platform

18.5 Conclusion

19 Probability of default for large corporates

19.1 Introduction

19.2 Model construction

19.3 BayesCredit

19.4 Model benchmarking

19.5 Benefits from technology and software

19.6 Conclusion

20 Risk management in robotics

20.1 Introduction

20.2 DeepC

20.3 The ADVOCATE II architecture

20.4 Model development

20.5 Model usage and examples

20.6 Benefits from using probabilistic graphical models

20.7 Conclusion

21 Enhancing Human Cognition

21.1 Introduction

21.2 Human foreknowledge in everyday settings

21.3 Machine foreknowledge

21.4 Current application and future research needs

21.5 Conclusion

22 Conclusion

22.1 An artificial intelligence perspective

22.2 A rational approach of knowledge

22.3 Future challenges

Bibliography

Index

Publish Book: 
Modify Date: 
Monday, May 19, 2008

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